Continuous glucose monitoring: determinants and prediction of diabetes mellitus development in the Framingham Heart Study

项目摘要 几乎一半的成年人患有糖尿病(DM)或糖尿病前期(PreDM)，但其中许多人 未确诊的病症。目前的糖尿病诊断和风险预测都是基于单一的“快照” 测量包括空腹血糖、餐后血糖和血红蛋白(Hb)A1c。然而， 一些没有被传统的DM生物标志物归入高危类别的人偶尔会 类似于糖尿病前期甚至糖尿病患者的血糖漂移。了解这些因素的决定因素 血糖模式及其是否会增加发生糖尿病的风险将有助于阐明糖尿病的病理生理学。 负责糖尿病的进展，并可改进糖尿病风险预测。我们将使用连续 血糖监测(CGM)，以测量2700名成年人(平均年龄58岁)的血糖模式 以社区为基础的弗雷明翰心脏研究第三代队列和OMNI 2，一个多种族队列。我们 目的描述大样本健康个体(大多数人不是)的标准血糖模式 有糖尿病)，探索标准的临床测量(体重指数、空腹血糖、糖化血红蛋白)、血液 代谢物、肠道微生物群、饮食模式、体力活动、糖尿病家族史和多基因风险评分 糖尿病与CGM衍生的血糖变量有关。我们将探讨的CGM派生变量包括时间 在血糖范围内(例如70-140毫克/分升)、高/低血糖发作、平均血糖和葡萄糖 可变性。此外，我们将检验CGM衍生的血糖变量是否可以预测糖尿病的发展。 超过2-3年的随访(通过每年自我报告的健康史)，并与患病率有关 心血管疾病(横断面)。我们的研究将提供可能改善预测的信息 发生糖尿病和糖尿病并发症的风险。我们的发现还可能带来新的发现，这些发现将量身定做并 糖尿病的靶向预防。

Project Abstract Almost half of adults have either diabetes mellitus (DM) or prediabetes (preDM), but many of those have undiagnosed conditions. Current DM diagnosis and risk prediction are based on single “snapshot” measurements, including fasting blood glucose, postprandial glucose, and hemoglobin (Hb)A1c. However, some individuals that do not fall into high-risk categories by traditional DM biomarkers have occasional glycemic excursions similar to individuals with preDM or even DM. Understanding the determinants of these glycemic patterns and whether they confer risk in developing DM will elucidate the pathophysiology responsible for the progression toward DM and could improve DM risk prediction. We will use continuous glucose monitoring (CGM) to measure glycemic patterns in 2700 adults (mean age 58 years) from the community-based Framingham Heart Study Third Generation cohort and Omni 2, a multi-ethnic cohort. We aim to describe normative glycemic patterns in a large sample of healthy individuals (most of whom do not have DM), exploring how standard clinical measures (body mass index, fasting blood glucose, HbA1c), blood metabolites, gut microbiome, dietary patterns, physical activity, family history of DM, and polygenic risk score for DM relate to CGM-derived glycemic variables. The CGM-derived variables we will explore include time spent in glycemic ranges (e.g. 70-140 mg/dL), hyper/hypoglycemic episodes, mean glucose and glucose variability. Furthermore, we will examine whether CGM-derived glycemic variables predict development of DM over 2-3 years follow-up (through an annual self-reported health history) and relate to prevalence of cardiovascular disease (cross-sectionally). Our study will provide information that could improve the prediction of developing DM and DM complications. Our findings may also lead to new discoveries that will tailor and target prevention of DM.